Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv”
Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv”"
Merge datasets

## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data

## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data

### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))

### FINISH THE CODE HERE ###
# load state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL

### FINISH THE CODE HERE
cv_states <- merge(cv_states, state_pops, by= "state")

2. Look at the data

Inspect the dimensions, head, and tail of the data
Inspect the structure of each variables. Are they in the correct format?

dim(cv_states)

## [1] 32250     9

head(cv_states)

##     state       date fips  cases deaths geo_id population pop_density abb
## 1 Alabama 2020-06-10    1  21989    744      1    4887871    96.50939  AL
## 2 Alabama 2021-10-31    1 832047  15573      1    4887871    96.50939  AL
## 3 Alabama 2021-05-26    1 542831  11138      1    4887871    96.50939  AL
## 4 Alabama 2020-04-19    1   4903    160      1    4887871    96.50939  AL
## 5 Alabama 2021-07-07    1 552911  11387      1    4887871    96.50939  AL
## 6 Alabama 2020-06-21    1  30021    839      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips cases deaths geo_id population pop_density abb
## 32245 Wyoming 2020-12-01   56 33805    230     56     577737    5.950611  WY
## 32246 Wyoming 2021-08-15   56 68272    793     56     577737    5.950611  WY
## 32247 Wyoming 2021-03-16   56 55352    693     56     577737    5.950611  WY
## 32248 Wyoming 2021-04-12   56 56988    701     56     577737    5.950611  WY
## 32249 Wyoming 2021-04-01   56 56389    700     56     577737    5.950611  WY
## 32250 Wyoming 2020-11-30   56 33305    215     56     577737    5.950611  WY

str(cv_states)

## 'data.frame':    32250 obs. of  9 variables:
##  $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ date       : IDate, format: "2020-06-10" "2021-10-31" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  21989 832047 542831 4903 552911 30021 117242 809485 134417 547135 ...
##  $ deaths     : int  744 15573 11138 160 11387 839 2037 14869 2285 11252 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : chr  "AL" "AL" "AL" "AL" ...

3. Format the data

Make date into a date variable
Make state into a factor variable
Order the data first by state, second by date
Confirm the variables are now correctly formatted
Inspect the range values for each variable. What is the date range? The range of cases and deaths?

# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")

# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)

### FINISH THE CODE HERE 
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]

# Confirm the variables are now correctly formatted
str(cv_states)

## 'data.frame':    32250 obs. of  9 variables:
##  $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2020-03-13" "2020-03-14" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
##  $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...

head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 215 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 428 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 69  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 496 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 341 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 53  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips  cases deaths geo_id population pop_density abb
## 31735 Wyoming 2021-11-06   56 105318   1243     56     577737    5.950611  WY
## 31791 Wyoming 2021-11-07   56 105318   1243     56     577737    5.950611  WY
## 31967 Wyoming 2021-11-08   56 105990   1243     56     577737    5.950611  WY
## 31942 Wyoming 2021-11-09   56 106287   1298     56     577737    5.950611  WY
## 31961 Wyoming 2021-11-10   56 106698   1298     56     577737    5.950611  WY
## 32016 Wyoming 2021-11-11   56 106698   1298     56     577737    5.950611  WY

# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 215 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 428 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 69  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 496 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 341 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 53  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

summary(cv_states)

##            state            date                 fips           cases        
##  Washington   :  661   Min.   :2020-01-21   Min.   : 1.00   Min.   :      1  
##  Illinois     :  658   1st Qu.:2020-08-03   1st Qu.:16.00   1st Qu.:  31774  
##  California   :  657   Median :2021-01-05   Median :29.00   Median : 146459  
##  Arizona      :  656   Mean   :2021-01-05   Mean   :29.78   Mean   : 386609  
##  Massachusetts:  650   3rd Qu.:2021-06-09   3rd Qu.:44.00   3rd Qu.: 481786  
##  Wisconsin    :  646   Max.   :2021-11-11   Max.   :72.00   Max.   :4993930  
##  (Other)      :28322                                                         
##      deaths          geo_id        population        pop_density       
##  Min.   :    0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
##  1st Qu.:  621   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
##  Median : 2658   Median :29.00   Median : 4468402   Median :  107.860  
##  Mean   : 7155   Mean   :29.78   Mean   : 6433897   Mean   :  422.513  
##  3rd Qu.: 8432   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
##  Max.   :73132   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
##                                                     NA's   :609        
##       abb       
##  WA     :  661  
##  IL     :  658  
##  CA     :  657  
##  AZ     :  656  
##  MA     :  650  
##  WI     :  646  
##  (Other):28322

min(cv_states$date)

## [1] "2020-01-21"

max(cv_states$date)

## [1] "2021-11-11"

4. Add `new_cases` and `new_deaths` and correct outliers

Add variables for new cases, new_cases, and new deaths, new_deaths:
- Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2
Filter to dates after June 1, 2021
Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
Correct outliers: Set negative values for new_cases or new_deaths to 0
Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths
Get the rolling average of new cases and new deaths to smooth over time
Inspect data again interactively

# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]

  # add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]

  ### FINISH THE CODE HERE
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1]
    cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }

  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}

# Focus on recent dates
cv_states <- cv_states %>% dplyr::filter(date >= "2021-06-01")

### FINISH THE CODE HERE
# Inspect outliers in new_cases using plotly
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)

p1<-NULL # to clear from workspace

p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

p2<-NULL # to clear from workspace

# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0


# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])

  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]

  ### FINISH CODE HERE
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)

# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

#p2=NULL

5. Add additional variables

Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:
- per100k = cases per 100,000 population
- newper100k= new cases per 100,000
- deathsper100k = deaths per 100,000
- newdeathsper100k = new deaths per 100,000
Add a “naive CFR” variable representing deaths / cases on each date for each state
Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture

### FINISH CODE HERE
# add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k =  as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))

# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))

# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))

II. Scatterplots

6. Explore scatterplots using `plot_ly()`

Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
- Color points by state and size points by state population
- Use hover to identify any outliers.
- Remove those outliers and replot.
Choose one plot. For this plot:
- Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
- Add layout information to title the chart and the axes
- Enable hovermode = "compare"

### FINISH CODE HERE

# pop_density vs. cases
cv_states_today %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# filter out "District of Columbia" since it's an outlier
cv_states_today_filter <- cv_states_today %>% filter(state!="District of Columbia")

# pop_density vs. cases after filtering
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# pop_density vs. deathsper100k
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# Adding hoverinfo
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , 
                         paste(" Deaths per 100k: ", deathsper100k, sep=""), sep = "<br>")) %>%
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
                  yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
         hovermode = "compare")

7. Explore scatterplot trend interactively using `ggplotly()` and `geom_smooth()`

For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()
Explore the pattern between \(x\) and \(y\) using geom_smooth()
- Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?

### FINISH CODE HERE
p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth()
ggplotly(p)

8. Multiple line chart

Create a line chart of the naive_CFR for all states over time using plot_ly()
- Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September. How have they changed over time?
Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()
- Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?

### FINISH CODE HERE
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")

### FINISH CODE HERE
# Line chart for Florida showing new_cases and new_deaths together
cv_states %>% filter(state=="Florida") %>% plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>% add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines")

9. Heatmaps

Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks

### FINISH CODE HERE
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% dplyr::filter(date>=as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL #get rid of date column
cv_states_mat2 <- as.matrix(cv_states_mat2) #heatmap requires input to be a matrix

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

# Repeat with newper100k
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% dplyr::filter(date>=as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")

cv_states_mat <- cv_states %>% select(state, date, newdeathsper100k) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newdeathsper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

10. Map

Create a map to visualize the naive_CFR by state on October 15, 2021
Compare with a map visualizing the naive_CFR by state on most recent date
Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this)
Describe the difference in the pattern of the CFR.

### For specified date

pick.date = "2021-10-15"

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states %>% filter(date==pick.date) %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", pick.date), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_pick.date <- fig

#############
### Map for today's date

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>%  select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig


### Plot together 
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05, which_layout = "merge")

Lab 11 - Interactive Visualization

11/12/2021

Learning Goals

Lab Description

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

2. Look at the data

3. Format the data

4. Add `new_cases` and `new_deaths` and correct outliers

5. Add additional variables

II. Scatterplots

6. Explore scatterplots using `plot_ly()`

7. Explore scatterplot trend interactively using `ggplotly()` and `geom_smooth()`

8. Multiple line chart

9. Heatmaps

10. Map

Lab 11 - Interactive Visualization

11/12/2021

Learning Goals

Lab Description

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

2. Look at the data

3. Format the data

4. Add new_cases and new_deaths and correct outliers

5. Add additional variables

II. Scatterplots

6. Explore scatterplots using plot_ly()

7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

8. Multiple line chart

9. Heatmaps

10. Map

4. Add `new_cases` and `new_deaths` and correct outliers

6. Explore scatterplots using `plot_ly()`

7. Explore scatterplot trend interactively using `ggplotly()` and `geom_smooth()`